Title: Image stitching algorithm based on fast feature extraction and perceptual hash

Authors: Ao-Mei Li; Zhe Wei; Wan-Li Jiang

Addresses: Department of Information Engineering, Army Artillery Air Defense Academy, 451 Huangshan Road, Hefei, Anhui, China ' Department of Information Engineering, Army Artillery Air Defense Academy, 451 Huangshan Road, Hefei, Anhui, China ' Army Artillery Air Defense Academy, 451 Huangshan Road, Hefei, Anhui, China

Abstract: The classic feature point image stitching algorithm is time-consuming in feature extraction, and an image sparse matrix method is proposed to determine the feature points. This method first uses Laplace operator to extract the image gradient and set the threshold to obtain the sparse matrix of image segmentation, then using features from accelerated segment test (FAST) detection algorithm for feature point. Finally, the speeded-up robust features (SURF) descriptor is given to increase the stability of the feature points. This method solves the problem of time-consuming feature extraction, making the real-time image mosaic possible. In the process of image mosaic, this paper presents a method using Kalman filter to predict the overlap region, thus reducing the computational complexity. In addition, because there is always a mismatch between feature points matching, this paper proposes a method using perceptual hashing to refine the matching pair, which solves the error caused by mismatching for the next image registration and improves the registration accuracy. Experimental results show that the proposed algorithm improves the speed and precision of image mosaic.

Keywords: image mosaic; sparse matrix; Laplace operator; FAST; Kalman filter; perception hash.

DOI: 10.1504/IJRIS.2019.102630

International Journal of Reasoning-based Intelligent Systems, 2019 Vol.11 No.3, pp.273 - 281

Received: 10 Feb 2018
Accepted: 22 Jan 2019
Published online: 30 Sep 2019 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article